Establishing Services & Resources to Support Faculty Writing Data

Management Plans: Lessons Learned from an Engineering Pilot

Jen Green & Natsuko NichollsPractice Paper Co-authors

Leena Lalwani, Paul Grochowski & Sara Samuel

IDCC14, San Francisco, CA24-27 Feb 2014

2

CONTEXT

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Data@UM

Strongly decentralized campus Grassroots works: deploying multiple data initiatives & projects

Capitalizing on liaison relationshipsDefining new roles for librarians

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College of Engineering

~16 departments, ~500 faculty, ~10,000 students

Strong liaison relationshipNew Responsible Conduct of Research program

Responsive leadership

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PROGRESSEngineering Data Management Support Pilot

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2004 2005 2006 2007 2008 2009 2010 2011 2012 201310%

15%

20%

25%

30%

35%

40%

26%

32%

29%

25%

31%

36%

26%27%

28%27%

24%23%

25%26%

25%

32%

23%22%

24%22%

NSF (Total) Funding Rate, Annual Trend, FY 2004-2013U of Michigan vs. All Institutions

U of Michigan Average of all institutions

Mean value: UM=29%; All institutions=25%

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2004 2005 2006 2007 2008 2009 2010 2011 2012 201310%

15%

20%

25%

30%

35%

40%

25%

17%

21%19%

26% 26%

21%20%

24%

27%

20%

17%18%

20% 20%

25%

18%17%

18%

21%

NSF (ENG) Funding Rate, Annual Trend, FY 2004-2013U of Michigan vs. All Institutions

U of Michigan Average of all institutions

Mean value: UM=23%; All institutions=19%

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FACULTY SURVEY

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Not at all familiar12%

Slightly famil-iar

20%

Moderately famil-iar

40%

Very familiar20%

N/A8%

Familiarity with DMP Requirements

10

Yes72%

No28%

Have you written a DMP?

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DMP ANALYSIS

DMP Analysis: Criteria

Roles and responsibilities for data management

Types of data produced Metadata standards Data storage options Plans for data sharing and redistribution Plans for archiving and preservation

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High38%

Med32%

Low21%

N/A9%

Level of Detail in DMPs

Good

19%

Fair65%

Poor

7%

N/A9%

Quality of DMPs

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No info 34%

Individuals (PIs, Co-PIs, stu-

dents)41%

Project Team

8%

Institutions8%

N/A9%

Roles & Responsibilities for Data Management

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No info74%

Detailed info9%

Some info9%

N/A9%

Volume of Data Generated

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No info30%

1-3 years25%

4-5 years15%

6-10 years6%

> 10 years15%

N/A9%

Period of Data Retention

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publication42%

faculty/project website

36%

conference presenta-

tion11%

"upon request"11%

Means of data dissemination

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Preservation

~20% indicated Deep Blue (UM’s IR) as a location for preservation of access to data.

DR was not mentioned.

No info5% One

op-tion20%Two op-

tions29%

More than three op-

tions38%

N/A9%

Data Storage Options

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RESOURCES & SUPPORTResearch Guides & Faculty Workshops

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Web-based NSF DMP Guide and Resources

http://guides.lib.umich.edu/engin-dmp

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22

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OTHER RESEARCHGraduate Student Interviews about Data

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< 1 GB 1 - 50 GB

51 - 100 GB

101 GB - 1 TB

1 - 2 TB > 2 TB

Se-ries1

0.464285714285714

0.285714285714286

0.0714285714285714

0.0714285714285714

0.0178571428571429

0.0625

5%

15%

25%

35%

45%

Note: N=109; Three students answered “Don’t know.”

Q. How much data did you generate in this research project?

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Q. “What percentage of your data would it take to replicate this research?”

0% 1 to 10%

11 to 20%

21 to 30%

31 to 40%

41 to 50%

51 to 60%

61 to 70%

71 to 80%

81 to 90%

91 to 100%

Series1

20 30 4 7 0 5 3 2 4 1 27

2.57.5

12.517.522.527.532.5

student counts

Note: N=103

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NEXT STEPS

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Lessons Learned & Next Steps

Lessons:• Engage stakeholders• Time

Next steps:• Integration into key required components• Increased graduate student offerings• DM Review Service development• Iterated pilot with LS&A

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Questions?

• Jen Green: Director of Research Data Services

greenjen@umich.edu

• Natsuko Nicholls: CLIR/DLF Data Curation Fellow

hayashin@umich.edu